The paper introduces Temporally Decomposable Image Representations (TDIR), a learning algorithm that splits historical photographs into orthogonal date and content components. It establishes theoretical conditions for this decomposition, quantifies errors when conditions are partially met, and demonstrates that temporal and categorical subspaces naturally become orthogonal during joint optimization. TDIR supports transitive operations, allowing temporal information from one image to be transferred to another without label supervision, and is validated on composed image retrieval tasks that combine object content and time period queries.
The paper introduces Image Bundle Composition (IBC), a new paradigm that moves beyond point-wise image matching to dynamically assemble cohesive image bundles from large, unstructured photo collections. It presents IBCBench, a benchmark with over 109,000 images and 667 verified queries, and proposes BundleWeaver, an agentic framework that uses a Large Language Model for relational role discovery and a Vision‑Language Model for bundle verification. Experiments show that BundleWeaver outperforms existing embedding and decompose‑and‑rerank methods, underscoring the importance of relational composition over atomic scoring.
By Rong Shan, Tianyi Xu, Congmin Zheng, Wenteng Chen, Jiachen Zhu, Junjie Wu, Teng Wang, Weiwen Liu, Changwang Zhang, Weinan Zhang, Jun Wang, Jianghao Lin
arXiv:2609.23881v1 Announce Type: new
Abstract: Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction....
By Markus Karmann, Shile Li, Christian Intern\`o, Bruno Andreis, David Klindt, Randall Balestriero, Jindong Gu, Philip Torr, Qi Zhang, Peng-Tao Jiang, Hao Zhang, Bo Li, Onay Urfalioglu
arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.
By Mantas Skackauskas, Xinyue Hao, Laura Sevilla-Lara
The paper introduces AllocEmbed, an allocate‑then‑embed framework that reallocates a fixed visual‑input budget across more video frames to improve retrieval performance. A lightweight allocator uses low‑cost previews to assign frame‑wise resolutions before the embedding backbone, preserving detail where it most benefits retrieval while reducing visual cost elsewhere. Retrieval‑Driven Policy Optimization (RDPO) learns the allocator directly from retrieval feedback, and the method integrates with existing systems without modifying the embedding model.
By Song Jin, Zhongtao Jiang, Chenglei Shen, Huanxuan Liao, Haozhe Chi, Zhiwei Wang, Kun Xu, Yong Liu
arXiv:2609.10008v1 Announce Type: new
Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gal...
By Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar, Abdelrahman Mohamed Shaker, Rao Anwer